To meet customer demand, the commercial printing industry requires the capability of producing spot colors and color images accurately and consistently. In a typical four color CMYK printer, when rendering a given color (Lab) on a CMYK printer, such as for spot color emulation, there is a range of CMYK values that will produce the desired Lab value. The available CMYK range is large for some colors (e.g., mid-tone neutrals) and small or zero for others (e.g., saturated colors). Although each CMYK value in the range will produce the desired Lab value, the printed spot colors with that recipe can differ widely in other attributes, such as graininess, mottle, color stability, ink cost, etc. Consequently, it is desirable to pick the CMYK recipe from among available recipes to optimize image quality. It can be computationally intensive to compute all possible CMYK recipes for a given color, select among these, and then configure the document reproduction device accordingly to a set of device-specific settings to achieve the desired image quality.
Moreover, an optimized CMYK recipe is considered useful when it not only produces accurate color but also renders colors that appear smoother (less noisy). It can be a challenging problem to determine the optimal CMYK recipe for target colors to achieve desired effects in multiple aspects, especially for the colors near the neutral axis. Customers often want to see accurate and smooth reproduction of color, as well as color match under different viewing conditions. Efforts have been made to improve smoothness of printed color patches. Some methods focus on single-objective optimization while others address multi-objective optimization based on weight assignments. Multi-objective optimization problems are often transformed into a single-objective optimization by assigning weights to the objectives. Finding a good set of weights is not trivial. It can be challenging for users to determine adequate weight distributions before initiating an optimization, especially for unskilled practitioners. Such a determination requires experience on the subject and a deep understanding of the effect of the weights on the respective objectives. Another approach is to set one objective as a primary objective and convert the remainder of objectives into appropriate constraints. Both techniques suffer from the fact that the customers have to decide the weight distribution or the constraints before initiating the optimization process and that only one solution is generated from the process.
Accordingly, what is needed in this art is increasingly sophisticated systems and methods for effectuating multi-objective optimization by providing users with a collection of optimal solutions to accommodate a range of user preferences in a manner where the selection of choices is easily understood and can be readily managed by customers.